Scheduling algorithm for job-shop robotic manufacturing cell problem with multi-robots

被引：0

作者：

Yang, Yujun ^{[1
]}

Long, Chuanze ^{[1
]}

Tao, Yu ^{[1
]}

机构：

[1] Guangdong Provincial Key Laboratory of Computer Integrated Manufacturing System, Guangdong University of Technology, Guangzhou

来源：

Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2015年 / 21卷 / 12期

基金：

中国国家自然科学基金;

关键词：

Disjunctive graph; Genetic algorithms; Job-shop; Manufacturing cell; Multi-robots; Neighborhood search; Scheduling;

D O I：

10.13196/j.cims.2015.12.016

中图分类号：

学科分类号：

摘要：

Based on the characteristics of job-shop robotic manufacturing cell scheduling problem, an improved genetic algorithm by integrating heuristic rules and neighborhood search strategy was researched, which was aimed at minimizing the maximum completion time. A mathematical optimization model and an improved disjunctive graph model for job-shop robotic manufacturing cell scheduling problem were established. Based on key path of disjunctive graph model, the moving machine block, changing robot block and adjusting robot task allocation were used to construct the search neighborhood, and the three layer scheduling method by integrating procedure insertion method and task allocation method of heuristic moving were used to initialize the population. Neighborhood structures-based local search algorithm was combined with three layer scheduling-based genetic algorithm to solve the problem effectively. Benchmark tests showed that the improved genetic algorithm was effective and was superior to other algorithms. © 2015, CIMS. All right reserved.

引用

页码：3239 / 3248

页数：9

共 13 条

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